Sli.do

https://www.sli.do/

Event code: 5421

Agenda

• gapminder
• dplyr
• ggplot2
• plotly
• shiny
• Case study

Introducing the data visualization eco-system in R

• magrittr for chaining functions(included in dplyr)
• gapminder for a great plotting data
• dplyr for data manipulation
• ggplot2 for data visualization
• plotly and shiny for interactive data visualization

We are gonna talk about them briefly

First of all, updating R to LTS version.

https://cran.r-project.org/

Installation and library these packages

pkgs <- c("gapminder", "dplyr", "ggplot2", "plotly", "shiny", "DT")
install.packages(pkgs)

library(gapminder)
library(dplyr)
library(ggplot2)
library(plotly)
library(shiny)
library(DT)

The story of Hans Rosling and Gapminder

https://youtu.be/jbkSRLYSojo

Basic functions in dplyr

• filter()
• select()
• arrange()
• mutate()
• summarise()
• group_by()

filter() for subsetting rows

gapminder %>% 
  filter(country == "Taiwan")

## # A tibble: 12 x 6
##    country continent  year lifeExp      pop gdpPercap
##    <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Taiwan  Asia       1952    58.5  8550362     1207.
##  2 Taiwan  Asia       1957    62.4 10164215     1508.
##  3 Taiwan  Asia       1962    65.2 11918938     1823.
##  4 Taiwan  Asia       1967    67.5 13648692     2644.
##  5 Taiwan  Asia       1972    69.4 15226039     4063.
##  6 Taiwan  Asia       1977    70.6 16785196     5597.
##  7 Taiwan  Asia       1982    72.2 18501390     7426.
##  8 Taiwan  Asia       1987    73.4 19757799    11055.
##  9 Taiwan  Asia       1992    74.3 20686918    15216.
## 10 Taiwan  Asia       1997    75.2 21628605    20207.
## 11 Taiwan  Asia       2002    77.0 22454239    23235.
## 12 Taiwan  Asia       2007    78.4 23174294    28718.

select() for extracting columns

gapminder %>%
  filter(country == "Taiwan") %>% 
  select(year, gdpPercap, lifeExp)

## # A tibble: 12 x 3
##     year gdpPercap lifeExp
##    <int>     <dbl>   <dbl>
##  1  1952     1207.    58.5
##  2  1957     1508.    62.4
##  3  1962     1823.    65.2
##  4  1967     2644.    67.5
##  5  1972     4063.    69.4
##  6  1977     5597.    70.6
##  7  1982     7426.    72.2
##  8  1987    11055.    73.4
##  9  1992    15216.    74.3
## 10  1997    20207.    75.2
## 11  2002    23235.    77.0
## 12  2007    28718.    78.4

arrange() for sorting rows based on certain variables

gapminder %>% 
  filter(continent == "Asia") %>% 
  filter(year == 2007) %>% 
  arrange(gdpPercap)

## # A tibble: 33 x 6
##    country          continent  year lifeExp        pop gdpPercap
##    <fct>            <fct>     <int>   <dbl>      <int>     <dbl>
##  1 Myanmar          Asia       2007    62.1   47761980      944 
##  2 Afghanistan      Asia       2007    43.8   31889923      975.
##  3 Nepal            Asia       2007    63.8   28901790     1091.
##  4 Bangladesh       Asia       2007    64.1  150448339     1391.
##  5 Korea, Dem. Rep. Asia       2007    67.3   23301725     1593.
##  6 Cambodia         Asia       2007    59.7   14131858     1714.
##  7 Yemen, Rep.      Asia       2007    62.7   22211743     2281.
##  8 Vietnam          Asia       2007    74.2   85262356     2442.
##  9 India            Asia       2007    64.7 1110396331     2452.
## 10 Pakistan         Asia       2007    65.5  169270617     2606.
## # ... with 23 more rows

mutate() for creating new columns

gapminder %>% 
  filter(country == "Taiwan") %>% 
  mutate(gdp_million = (gdpPercap * pop / 1000000))

## # A tibble: 12 x 7
##    country continent  year lifeExp      pop gdpPercap gdp_million
##    <fct>   <fct>     <int>   <dbl>    <int>     <dbl>       <dbl>
##  1 Taiwan  Asia       1952    58.5  8550362     1207.      10320.
##  2 Taiwan  Asia       1957    62.4 10164215     1508.      15326.
##  3 Taiwan  Asia       1962    65.2 11918938     1823.      21727.
##  4 Taiwan  Asia       1967    67.5 13648692     2644.      36085.
##  5 Taiwan  Asia       1972    69.4 15226039     4063.      61856.
##  6 Taiwan  Asia       1977    70.6 16785196     5597.      93939.
##  7 Taiwan  Asia       1982    72.2 18501390     7426.     137398.
##  8 Taiwan  Asia       1987    73.4 19757799    11055.     218414.
##  9 Taiwan  Asia       1992    74.3 20686918    15216.     314765.
## 10 Taiwan  Asia       1997    75.2 21628605    20207.     437045.
## 11 Taiwan  Asia       2002    77.0 22454239    23235.     521734.
## 12 Taiwan  Asia       2007    78.4 23174294    28718.     665526.

summarise() for a summary

gapminder %>% 
  summarise(median(gdpPercap))

## # A tibble: 1 x 1
##   `median(gdpPercap)`
##                 <dbl>
## 1               3532.

group_by() for a grouped summary

gapminder %>% 
  group_by(continent) %>% 
  summarise(medianGdpPercap = median(gdpPercap))

## # A tibble: 5 x 2
##   continent medianGdpPercap
##   <fct>               <dbl>
## 1 Africa              1192.
## 2 Americas            5466.
## 3 Asia                2647.
## 4 Europe             12082.
## 5 Oceania            17983.

Going further with dplyr

https://dplyr.tidyverse.org/

gg stands for…

Grammar of graphics.

Basic concepts

• ggplot(aes(x = , y = , color = , fill = , ...)) for data mapping
• geom_OOO() for different charts`
• Using + to add different layers

geom_point() for exploring correlations

plt <- gapminder %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Rendering scatter plot

plt

geom_histogram() for exploring distributions

plt <- gapminder %>% 
  ggplot(aes(x = gdpPercap)) +
  geom_histogram(bins = 40)

Rendering histogram

plt

geom_bar() for exploring row counts

plt <- gapminder %>% 
  filter(year == 2007) %>%
  ggplot(aes(x = continent)) +
  geom_bar()

Rendering bar plot

plt

geom_bar() for grouped summary

plt <- gapminder %>% 
  filter(year == 2007) %>% 
  group_by(continent) %>% 
  summarise(medianGdpPercap = median(gdpPercap)) %>% 
  ggplot(aes(x = continent, y = medianGdpPercap, fill = continent)) +
  geom_bar(stat = "identity")

Rendering another bar plot

plt

Going further with ggplot2

https://ggplot2.tidyverse.org/

About plotly

Create interactive, D3 and WebGL charts in R.

Quickstart with plotly

Converting ggplot2 graphs to interactive versions with ggplotly().

Converting our last bar plot

p <- ggplotly(plt)

Rendering interactive bar plot

p

Plotting a gapminder replica with plotly

radius <- sqrt((gapminder$pop)/pi)

p <- gapminder %>%
  plot_ly(
    x = ~gdpPercap, 
    y = ~lifeExp, 
    size = ~pop, 
    color = ~continent, 
    frame = ~year, 
    text = ~country, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers',
    sizes = c(min(radius), max(radius))
  ) %>%
  layout(
    xaxis = list(
      type = "log"
    )
  )

Rendering gapminder replica with plotly

p

About shiny

Shiny is an R package that makes it easy to build interactive web applications (apps) straight from R.

What is a Shiny app

• Shiny apps are contained in a single script called app.R
• app.R lives in a directory (for example, newdir/) and the app can be run with runApp(“newdir”)

app.R has three components

• a user interface object
• a server function
• a call to the shinyApp function

How do these three components collaborate

• The user interface (ui) object controls the layout and appearance
• The server function contains the instructions that your computer needs to build your app
• The shinyApp() function creates Shiny app objects

A hello shiny app

# app.R
library(gapminder)
library(shiny)

ui <- fluidPage(

  # App title ----
  titlePanel("Hello Shiny!"),

  # Sidebar layout with input and output definitions ----
  sidebarLayout(

    # Sidebar panel for inputs ----
    sidebarPanel(

      # Input: Slider for the number of bins ----
      sliderInput(inputId = "bins",
                  label = "Number of bins:",
                  min = 1,
                  max = 50,
                  value = 30)

    ),

    # Main panel for displaying outputs ----
    mainPanel(

      # Output: Histogram ----
      plotOutput(outputId = "histPlot")

    )
  )
)

# Define server logic required to draw a histogram ----
server <- function(input, output) {

  # Histogram of the Old Faithful Geyser Data ----
  # with requested number of bins
  # This expression that generates a histogram is wrapped in a call
  # to renderPlot to indicate that:
  #
  # 1. It is "reactive" and therefore should be automatically
  #    re-executed when inputs (input$bins) change
  # 2. Its output type is a plot
  output$histPlot <- renderPlot({

    x    <- gapminder$gdpPercap
    bins <- seq(min(x), max(x), length.out = input$bins + 1)

    hist(x, breaks = bins, col = "#75AADB", border = "white",
         xlab = "GDP Per Capita",
         main = "Histogram of GDP Per Capita")

    })

}
shinyApp(ui = ui, server = server)

Creating a gapminder replica with shiny and plotly

# app.R
# Library packages
# install.packages(c("gapminder", "plotly", "shiny"))
library(gapminder)
library(plotly)
library(shiny)
library(dplyr)

# Globar variables
bubble_radius <- sqrt(gapminder$pop / pi)
unique_continents <- unique(gapminder$continent)
range_gdpPercap <- log10(range(gapminder$gdpPercap) + c(-200, 20000))
range_lifeExp <- range(gapminder$lifeExp) + c(-30, 30)

# Define UI for application
ui <- fluidPage(
   # Application title
   titlePanel("R Gapminder Replica"),
   # Sidebar panel
   sidebarLayout(
     # CheckboxGroup input
     sidebarPanel(
       checkboxGroupInput(
         "continents",
         "Continents:",
         choices = unique_continents,
         selected = unique_continents
       )
     ),
     # Plotly rendering
     mainPanel(
       plotlyOutput("gapminder_bubble")
     )
   )
   
)

# Define server logic
server <- function(input, output) {
  # reactive filtering
  reactive_gapminder <- reactive(
    gapminder %>%
      filter(continent %in% input$continents)
  )
  output$gapminder_bubble <- renderPlotly({
    validate(
      need(input$continents, 'Check at least one continent!')
    )
    reactive_gapminder() %>% 
       plot_ly(x = ~gdpPercap, y = ~lifeExp,
               size = ~pop, type = "scatter", mode = "markers",
               color = ~continent, text = ~country, frame = ~year, hoverinfo = "text",
               sizes = c(min(bubble_radius), max(bubble_radius))) %>%
       layout(xaxis = list(type = "log",
                           range = range_gdpPercap),
              yaxis = list(range = range_lifeExp))
   })
}

# Run the application 
shinyApp(ui = ui, server = server)

Getting our case study data

mayor <- readRDS(url("https://s3-ap-northeast-1.amazonaws.com/tw-election-2018/mayor.rds"))
dim(mayor)
head(mayor)
tail(mayor)
summary(mayor)
str(mayor)

## [1] 62689     7
##   admin_area district village office votes  party candidate
## 1     台北市   北投區  建民里      1     4 無黨籍    吳蕚洋
## 2     台北市   北投區  建民里      2     2 無黨籍    吳蕚洋
## 3     台北市   北投區  建民里      3     2 無黨籍    吳蕚洋
## 4     台北市   北投區  文林里      4     1 無黨籍    吳蕚洋
## 5     台北市   北投區  文林里      5     5 無黨籍    吳蕚洋
## 6     台北市   北投區  文林里      6     3 無黨籍    吳蕚洋
##       admin_area district                        village office votes
## 62684     連江縣   南竿鄉 馬祖村、津沙村、四維村、仁愛村      4   654
## 62685     連江縣   北竿鄉         后沃村、橋仔村、塘岐村      5   838
## 62686     連江縣   北竿鄉         坂里村、白沙村、芹壁村      6   301
## 62687     連江縣   莒光鄉         田沃村、西坵村、青帆村      7   341
## 62688     連江縣   莒光鄉                 大坪村、福正村      8   391
## 62689     連江縣   東引鄉                 樂華村、中柳村      9   396
##            party candidate
## 62684 中國國民黨    劉增應
## 62685 中國國民黨    劉增應
## 62686 中國國民黨    劉增應
## 62687 中國國民黨    劉增應
## 62688 中國國民黨    劉增應
## 62689 中國國民黨    劉增應
##   admin_area          district           village              office      
##  Length:62689       Length:62689       Length:62689       Min.   :   1.0  
##  Class :character   Class :character   Class :character   1st Qu.: 190.0  
##  Mode  :character   Mode  :character   Mode  :character   Median : 457.0  
##                                                           Mean   : 602.8  
##                                                           3rd Qu.: 934.0  
##                                                           Max.   :2446.0  
##      votes           party            candidate        
##  Min.   :   0.0   Length:62689       Length:62689      
##  1st Qu.:  12.0   Class :character   Class :character  
##  Median : 148.0   Mode  :character   Mode  :character  
##  Mean   : 199.5                                        
##  3rd Qu.: 358.0                                        
##  Max.   :1237.0                                        
## 'data.frame':    62689 obs. of  7 variables:
##  $ admin_area: chr  "台北市" "台北市" "台北市" "台北市" ...
##  $ district  : chr  "北投區" "北投區" "北投區" "北投區" ...
##  $ village   : chr  "建民里" "建民里" "建民里" "文林里" ...
##  $ office    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ votes     : int  4 2 2 1 5 3 0 4 5 3 ...
##  $ party     : chr  "無黨籍" "無黨籍" "無黨籍" "無黨籍" ...
##  $ candidate : chr  "吳蕚洋" "吳蕚洋" "吳蕚洋" "吳蕚洋" ...

Try creating a visualization by yourself!